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Abstract

Many governments are reducing conventional energy production using natural gas or coal as they spread much CO2 to into the atmosphere, leading to global warming. Moreso, with a sharp increase in electricity demand, renewable energy sources are gaining more popularity around the world. A renewable energy source such as solar energy offers one of the easiest ways of generating renewable energy as it can be done by anyone/household through the installation of solar panels in an open place such as house roofs. Therefore, deleting the centralized exchange of the generated energy by the household will provide huge economic benefits as well as optimize the flow of renewable energy. However, the current conventional centralized energy trading makes it impossible for individuals (prosumers) to participate because of the need for third parties to enable transactions which in turn adds extra financial burden and discomfort. To eradicate the need for a third party through decentralization while ensuring non-comparability, transparency, security, and integrity of transactions, previous works have proposed the integration of blockchain systems into the trading system. However, to ensure the sustainability of blockchain technology for trading, there is a need to consider energy consumption and prediction in the system to enable planning about the energy demand.

Keywords

consider consumption prediction

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How to Cite
Babakulov Bekzod Mamatkulovich, To’rabekov Muhammad Alisher o’g’li, Yusupov Adxamjon Dilshod o’gli, & Abdualimov Akbar Akmal o’g’li. (2023). Predicting daily energy production in a blockchain-based P2P energy trading system. Texas Journal of Engineering and Technology, 18, 7–11. Retrieved from https://zienjournals.com/index.php/tjet/article/view/3566

References

  1. [1] “Renewable and Non-renewable Energy Resources Explained | KQED.” https://www.kqed.org/science/renewable-and-non-renewable-energy-resources-explained (accessed Aug. 12, 2020).
  2. [2] “Advantages and Disadvantages of Renewable Energy | EnergySage.” https://news.energysage.com/advantages-and-disadvantages-of-renewable-energy/ (accessed Aug. 12, 2020).
  3. [3] Mamatkulovich B. B. AUTOMATIC STUDENT ATTENDANCE SYSTEM USING FACE RECOGNITON //Next Scientists Conferences. – 2022. – С. 6-22..
  4. [4] N. Z. Aitzhan and D. Svetinovic, “Security and Privacy in Decentralized Energy Trading Through Multi-Signatures, Blockchain and Anonymous Messaging Streams,” IEEE Trans. Dependable Secur. Comput., vol. 15, no. 5, pp. 840–852, 2018, doi: 10.1109/TDSC.2016.2616861.
  5. [5] X. Lu et al., “Blockchain-Based Distributed Energy Trading in Energy Internet: An SDN Approach,” IEEE Access, vol. 7, pp. 173817–173826, 2019, doi: 10.1109/ACCESS.2019.2957211.
  6. [6] Mamatkulovich B. B. LIGHTWEIGHT RESIDUAL LAYERS BASED CONVOLUTIONAL NEURAL NETWORKS FOR TRAFFIC SIGN RECOGNITION //European International Journal of Multidisciplinary Research and Management Studies. – 2022. – Т. 2. – №. 05. – С. 88-94..
  7. [7] R. Chaudhary, A. Jindal, G. S. Aujla, S. Aggarwal, N. Kumar, and K. K. R. Choo, “BEST: Blockchain-based secure energy trading in SDN-enabled intelligent transportation system,” Comput. Secur., vol. 85, pp. 288–299, 2019, doi: 10.1016/j.cose.2019.05.006.
  8. [8] Z. Guan, X. Lu, N. Wang, J. Wu, X. Du, and M. Guizani, “Towards secure and efficient energy trading in IIoT-enabled energy internet: A blockchain approach,” Futur. Gener. Comput. Syst., no. xxxx, 2019, doi: 10.1016/j.future.2019.09.027.
  9. [9] S. Wang, A. F. Taha, J. Wang, K. Kvaternik, and A. Hahn, “Energy Crowdsourcing and Peer-to-Peer Energy Trading in Blockchain-Enabled Smart Grids,” in IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019, vol. 49, no. 8, pp. 1612–1623, doi: 10.1109/TSMC.2019.2916565.
  10. [10] Amrullayevich K. A., Mamatkulovich B. B. TALABALARDA AXBOROT BILAN ISHLASH KOMPETENTSIYASINI SHAKILLANTIRISHDA DIDAKTIK VOSITALARINING METODIK XUSUSIYATLARIDAN FOYDALANISH //International Journal of Contemporary Scientific and Technical Research. – 2022. – С. 645-650..
  11. [11] S. Saxena, H. Farag, A. Brookson, H. Turesson, and H. Kim, “Design and Field Implementation of Blockchain Based Renewable Energy Trading in Residential Communities,” 2019, doi: 10.1109/SGRE46976.2019.9020672.
  12. [12] Babakulov B. UNİVERSİTET TALABALARİ UCHUN CHUQUR O'RGANİSHGA ASOSLANGAN YUZNİ ANİQLASHDAN FOYDALANGAN HOLDA AVTOMATİK DAVOMAT TİZİMİ //Zamonaviy dunyoda innovatsion tadqiqotlar: Nazariya va amaliyot. – 2023. – Т. 2. – №. 3. – С. 74-76.
  13. [13] M. Gao, J. Li, F. Hong, and D. Long, “Day-ahead power forecasting in a large-scale photovoltaic plant based on weather classification using LSTM,” Energy, vol. 187, p. 115838, 2019, doi: 10.1016/j.energy.2019.07.168.
  14. [14] M. Paulescu, M. Brabec, R. Boata, and V. Badescu, “Structured, physically inspired (gray box) models versus black box modeling for forecasting the output power of photovoltaic plants,” Energy, vol. 121, pp. 792–802, 2017, doi: 10.1016/j.energy.2017.01.015.
  15. [15] Mamatkulovich B. B. A DESIGN OF SMALL SCALE DEEP CNN MODEL FOR FACIAL EXPRESSION RECOGNITION USING THE LOW-RESOLUTION IMAGE DATASETS //MODELS AND METHODS FOR INCREASING THE EFFICIENCY OF INNOVATIVE RESEARCH. – 2023. – Т. 2. – №. 19. – С. 284-288.
  16. [16] A. Mellit and A. Massi, “A 24-h forecast of solar irradiance using artificial neural network : Application for performance prediction of a grid-connected PV plant at Trieste , Italy,” Sol. Energy, vol. 84, no. 5, pp. 807–821, 2010, doi: 10.1016/j.solener.2010.02.006.
  17. [17] C. Shang and P. Wei, “Enhanced support vector regression based forecast engine to predict solar power output,” Renew. Energy, vol. 127, pp. 269–283, 2018, doi: 10.1016/j.renene.2018.04.067.
  18. [18] W. Lee and P. Wang, “I �,” pp. 7–12, 2011.
  19. [19] J. Shi, W. J. Lee, Y. Liu, Y. Yang, and P. Wang, “Forecasting power output of photovoltaic systems based on weather classification and support vector machines,” IEEE Trans. Ind. Appl., vol. 48, no. 3, pp. 1064–1069, 2012, doi: 10.1109/TIA.2012.2190816.
  20. [20] A. T. Eseye, J. Zhang, and D. Zheng, “Short-term photovoltaic solar power forecasting using a hybrid Wavelet-PSO-SVM model based on SCADA and Meteorological information,” Renew. Energy, vol. 118, pp. 357–367, 2018, doi: 10.1016/j.renene.2017.11.011.
  21. [21] A. Gensler, J. Henze, B. Sick, and N. Raabe, “Deep Learning for solar power forecasting - An approach using AutoEncoder and LSTM Neural Networks,” in 2016 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016 - Conference Proceedings, 2017, pp. 2858–2865, doi: 10.1109/SMC.2016.7844673.
  22. [22] D. L. Marino, K. Amarasinghe, and M. Manic, “Building Energy Load Forecasting using Deep Neural Networks,” pp. 7046–7051, 2016.